1. Field of the Invention
The present invention relates to digital image processing and, more particularly, to processing continuous-tone images into halftone images.
2. Description of the Related Art
Digital Halftoning is the process of transforming a continuous-tone image into a binary image that has the illusion of the original continuous-tone image. See, R. Ulchney, Digital Halftoning, MIT Press, Cambridge, Mass, 1987. In the case of color images, the color continuous-tone image is typically separated into color channels first. Separate halftones are then formed for each of the color channels.
Error diffusion (see, R. W. Floyd and L. Steinberg, "An adaptive Algorithm for Spatial Greyscale", Proc. SID, 17:75-77, 1976) is one important class of digital halftoning algorithms that renders continuous-tone images by thresholding their gray levels and distributing errors caused by the thresholding to neighboring unprocessed pixels. Error diffusion is good at producing image details. However, in flat regions, it often has visible artifacts such as worms that are difficult to eliminate.
Smooth dithers, on the other hand, are a class of halftoning methods that produce smooth textures in flat regions, but usually are less sharp for lack of feedback. One example of a smooth dither is Color Smooth Dither (CSD) (see, J. P. Allebach and Q. Lin, "Joint Design of Dither Matrices for a Set of Colorants", U.S. patent application Ser. No. 08/641,304, filed Apr. 30, 1996). Another example of a smooth dither is Super Smooth Dither (SSD) (see, Q. Lin, "Halftone Image Formation Using Dither Matrix Generated Based Upon Printed Symbol Models", U.S. Pat. No. 5,469,515, issued Nov. 21, 1995; Q. Lin, "Halftone Images Using Special Filters", U.S. Pat. No. 5,317,418, issued May 31, 1994).
Different halftoning algorithms are best for different types of images and different types of printers. For example, Table 1 shows the optimal halftoning algorithms for different image regions on a typical inkjet printer that is printing computer generated graphics. In the case of computer generated graphics, it is possible to select the optimal halftoning technique for a particular image because information about the type of image being halftoned is known to the print driver.
TABLE 1 ______________________________________ Optimal Halftoning Algorithms Computer Generated Graphics (object type known to the driver) ______________________________________ text error diffusion line art error diffusion area fill smooth dither ______________________________________
Proper selection of a halftoning technique is especially important in rendering a scanned document, where there is a mixture of text, line art and area fill, and raster image. For example, Table 2 shows the optimal halftoning algorithms for different image regions on a typical inkjet printer that is printing a scanned document.
TABLE 2 ______________________________________ Optimal Halftoning Algorithms Scanned Document (object type not known to driver) ______________________________________ text error diffusion line art error diffusion area fill smooth dither busy image region error diffusion smooth image region smooth dither ______________________________________
Similarly, proper selection of a halftoning technique is also important when rendering a digital photographic image that has a mixture of detailed regions and uniformly smooth colored regions. However, for images such as scanned photographic images, the print driver cannot select the best halftoning technique because it does not have any information regarding the composition of the page.
Thus, it can be seen that halftone imaging techniques impose image quality limits upon halftone image output devices, and hinder the use of these devices in many applications.
Therefore, there is an unresolved need for a technique that can improve halftone imaging by integrating different halftoning algorithms and managing their transitions based on image content.